1. Field of the Invention
The invention relates to a method of processing a digital image representing ribbon-shaped objects of non-uniform intensity contrasting with a background of smaller intensity, which method includes an automatic segmentation phase, and also relates to a device for a medical system, which device carries out this method. The invention is applied, in particular, in the medical imaging systems industry, such as in arterial X-ray imaging systems, and more particularly, to cerebrovascular imaging systems.
2. Description of Related Art
An image processing system for the segmentation of a coronary arteriogram and for automatically determining the arterial skeleton and borders in this arteriogram is known from the publication entitled "A fully automated identification of coronary borders from the tree structure of coronary angiograms" by Chien-Chuan KO, Chi-Wu MAO, Yung-Nien SUN, and Shei-Hsi CHANG, in the International Journal of Bio-Medical Computing, 39 (1995), pp. 193-208, published by ELSEVIER.
This method comprises the acquisition of a digital angiographic image and the segmentation of this image for separating information relating to objects formed by the arteries from the rest or background of the image. The segmentation step comprises a sequence of morphological operations. This sequence includes first of all the application of a median filter to the original image to remove noise while preserving the object borders. The smoothed image is subsequently processed by morphological filters.
For processing the image the arteries are shown as bright representations on a dark background. A morphological filter is first applied to said smoothed image to identify background portions having slow intensity variations. This filter effects a morphological closing operation using a flat hexagonal morphological structuring element having a diameter of 25 pixels. A flat morphological structuring element means that the intensity data inside the filter limits is constant. The diameter of 25 pixels is greater than the diameter of the arteries in the original image. By means of this operation arteries narrower than the hexagon are removed from the smoothed image. The resulting image is then subtracted from the smoothed image to produce a new image which still retains the object edge information. This morphological closing operation forms a background extraction operation. For this purpose, it includes a dilation operation followed by an erosion operation.
This background extraction operation by morphological closing is followed by a linear intensity normalization to enhance the contrast and subsequently by a thresholding operation to produce a binary image.
The actual segmentation of the image into objects and background is now performed on this binary image. It includes a binary closing operation to smooth the contours of the coronary artery and to restore vessel sections broken during the background extraction process. It further comprises a binary opening operation applied to the preceding image, in order to remove extraneous objects from the arterial system. In these two morphological closing and opening operations the structuring elements are flat. The operations are based on contours.
In all the morphological operations performed, the algorithms are iterative and require much computing time.
It is at this stage where the image segmentation in accordance with the method known from the document cited ends.
The known method relates more particularly to a contrast-rich original image where the arteries can be distinguished clearly or where there is a comparatively small amount of overlap of arteries, the arterial system rather having a tree shape, and where anomalies such as stenoses are to be detected.